Data Scientist

• Solve business problems using data
• Analyze and interpret data to identify patterns or trends
• Pull data from SQL databases
• Visualize data using Tableau
• Present findings to stakeholders

Data Scientist II

• You'll sit on our central Data Science & Analytics team but you’ll be embedded in one of Jerry's core product or business areas — growth, product, AI and automation, strategic partnerships — and own the analytical function for that business unit end-to-end.
• Define the right problems: Before any analysis, understand what business question it's answering and what decision it will inform. Push back if the question is flawed.
• Define metrics, build reports, run analyses, design experiments, and surface data insights that move your team's key metrics.
• Bring recommendations to your counterparts in product, growth, finance or operations, tell them what's happening and why, and what next steps should be.

Associate Data Scientist

• You'll sit on our central Data Science & Analytics team but you’ll be embedded in one of Jerry's core product or business areas — growth, product, AI and automation, strategic partnerships — and own the analytical function for that business unit end-to-end.
• Define the right problems: Before any analysis, understand what business question it's answering and what decision it will inform. Push back if the question is flawed.
• Data and insights: Define metrics, build reports, run analyses, design experiments, and surface data insights that move your team's key metrics.
• Drive outcomes: Bring recommendations to your counterparts in product, growth, finance or operations, tell them what's happening and why, and what next steps should be.

Senior Data Scientist

• Define the right problems: Before any analysis, understand what business question it's answering and what decision it will inform. Push back if the question is flawed.
• Data and insights: Define metrics, build reports, run analyses, design experiments, and surface data insights that move your team's key metrics.
• Drive outcomes: Bring recommendations to your counterparts in product, growth, finance or operations, tell them what's happening and why, and what next steps should be.

Staff Data Scientist

• Lead the architecture and technical strategy for large-scale retrieval, ranking, vector search, RAG, and anomaly detection systems operating at enterprise scale
• Drive innovation across LLMs, AI-powered discovery, personalization, and autonomous AI workflows
• Define the future direction of AI-driven reasoning, ranking and investigation capabilities across the platform
• Own end-to-end AI/ML architecture, including data pipelines, feature engineering, model development, deployment, monitoring, evaluation, and continuous improvement
• Establish scalable architecture patterns, engineering standards, experimentation frameworks, and operational best practices across multiple teams
• Drive foundational investments and technical direction across AI platforms, retrieval infrastructure, model-serving systems, and ML tooling
• Balance model quality, customer experience, latency, scalability, reliability, security, and infrastructure cost when making architectural decisions
• Define evaluation methodologies, experimentation strategies, and success metrics to assess product, model, and business impact
• Partner closely with Product, Engineering, Security, Infrastructure, and Operations teams to identify high-value opportunities and deliver scalable AI solutions
• Communicate technical trade-offs, recommendations, and results clearly to both technical and executive audiences
• Lead highly ambiguous, multi-quarter initiatives spanning Product, Engineering, Data Science, and other stakeholders
• Influence organization-wide technical strategy, investment priorities, and long-term AI roadmap decisions
• Represent the organization in executive reviews and drive alignment on major technical and product initiatives
• Mentor junior scientists while fostering a culture of technical excellence and innovation

Senior Data Scientist – Hybrid

• Own recruiting analytics end-to-end
• Build headcount + velocity models
• Make talent data decision-grade
• Bring signal to pipeline leadership reviews
• Improve the recruiting system
• Use AI as leverage in your first 90 days

People Analytics Lead – Recruiting

• Own enterprise data science strategy and multi-year roadmap.
• Prioritize high-impact use cases with executive stakeholders.
• Oversee model lifecycle, from discovery to productionization.
• Establish enterprise level standards for MLOps and responsible AI governance.
• Develop talent, structure teams, and manage budgets.
• Communicate results and risks to senior leadership.

Senior Director, Data Science

• Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI powered products that change how customers interact with their money.
• Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Hugging Face, LangChain, Lightning, VectorDBs, and more to reveal the insights hidden within huge volumes of numeric and textual data.
• Be the expert in Natural Language Processing (NLP) to harness the power of Large Language Models (LLMs), adapt and finetune them for customer facing applications and features.
• Build machine learning and NLP models through all phases of development, from design through training, evaluation, and validation; partnering with engineering teams to operationalize them in scalable and resilient production systems that serve 80+ million customers.
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

Senior Manager, Data Science – AI Foundations

• Own attribution, incrementality, LTV / CAC, customer segmentation, forecasting, and experimentation across our business units.
• Build the analytical view of how our businesses (admin, Carry, Ark, Meridian) reinforce each other, where the flywheel is real, where it’s aspirational, and what investments accelerate it.
• Make running good experiments the default for product, marketing, and growth, with the infrastructure and review process to back it up.
• Inherit a working platform with real gaps. Decide which gaps to fill, which to accept, and how to keep investing so the foundation grows with the demands you’re putting on it.
• Lead a small, senior team of three to start, and personally do a meaningful share of the work and shaping of the team.

Head of Data

• Collaborate with business partners including commercial insights and analytics, brand teams, sales operations, data teams, external vendor partners, media agencies, service owners, and IS partners to create solutions and insights to solve challenging business problems across multiple therapeutic areas and commercial functions.
• Apply deep technical expertise to manipulate large messy datasets, write high-quality code, and adapt complicated machine learning models to the problems at-hand.
• Transform business, medical, or scientific questions into analytical ones and mapping out solutions, delivery, and impact.
• Identify business needs and proposing potential analytical approaches for solutions.
• Build high-performance algorithms, prototypes, predictive models, and proof of concepts using Python or R with reliable performance, interpretability, and action-ability.
• Effectively communicate cross-functionally with stakeholders to develop appropriate methodology to answer specific business questions.
• Help further build the team, including by contributing ideas and standard methodologies and keeping abreast of developments in industry and academia.
• Create powerful stories in PowerPoint, with knowledge of MS Office Suite (Excel and PowerPoint).

CD&A Data Scientist

• Architect and own advanced modeling strategies across fraud and abuse problem domains.
• Establish and defend model quality standards that account for adversarial dynamics.
• Lead statistical innovation on highest-leverage fraud problems.
• Partner with ML engineering and information security on adversarial robustness.
• Build automated workflows that scale human expertise while respecting fraud complexity.

• Define Ads ontologies and metrics for campaigns, budgets, bids, creatives, audiences, and placements.
• Build dbt models and curated marts in Snowflake with clear data contracts, tests, and SLOs.
• Ingest and enrich unstructured Ads content and publish retrieval‑ready datasets using our managed search/vector services.
• Design and evaluate retrieval workflows (RAG) with existing services for hybrid search and re‑ranking; set quality/latency targets and iterate via experiments.
• Design agent reasoning and policies on ads, including tool definitions and human‑in‑the‑loop approvals.
• Establish evaluation suites covering precision/recall, calibration, hallucination rate, latency, and cost.
• Run A/B or uplift experiments to quantify impact and guide iteration.
• Translate Ads problems into agent behaviors and own KPIs such as ROAS lift, pacing accuracy, RCA precision/recall, forecast MAPE, and time‑to‑insight.

Ads AI Analytics Lead

• Own clinical data quality across claims, EHR, and ADT: Define standards for how clinical data is structured, normalized, and validated as modeling inputs across payer claims (medical, pharmacy, eligibility), EHR data (Epic, Cerner, Athena), and real-time ADT feeds. Bring deep familiarity with EHR data formats (FHIR, HL7, C-CDA) and how data from systems like Epic, Cerner, and Athena maps to clinical reality. Hold the bar for clinical accuracy and completeness across all three sources.
• Build and ship production ML/AI models: Develop, validate, and deploy risk stratification, care gap prediction, treatment effect estimation, and LLM/foundation model applications — with rigor around leakage, calibration, fairness, and clinical face validity.
• Apply health economics and outcomes methods: Translate raw clinical and claims data into decision-grade evidence through risk adjustment, utilization measurement, cost attribution, quasi-experimental evaluation, and outcomes measurement aligned with CMS, NCQA, and MCO reporting standards.
• Advance machine and AI products: Bring senior modeling judgment to the product roadmap, owning the clinical and methodological soundness of what ships.
• Set standards and mentor: Make architectural trade-offs, drive alignment across data science, engineering, product, and clinical stakeholders, and mentor junior data scientists to raise the technical bar of the team.

Principal Data Scientist, Health Informatics

• Lead and develop a team of data scientists working on utilization forecasting and care routing optimization
• Define and execute the data science roadmap supporting forecasting accuracy, experimentation, and decision systems
• Build and productionize forecasting models that predict care utilization across customer lifecycle stages
• Develop and iterate on routing algorithms and experimentation frameworks to influence member care pathways
• Partner with Product and Engineering to translate models into scalable, real-time systems
• Collaborate with Actuarial and Finance teams to align forecasts with pricing assumptions and business targets
• Design and analyze experiments to evaluate interventions that shift member behavior and care utilization
• Establish monitoring systems to detect deviations in utilization trends and trigger interventions
• Communicate insights, risks, and trade-offs clearly to senior stakeholders
• Ensure high standards for model quality, documentation, and reproducibility

Manager, Data Science, Forecasting – Routing

• Serve as the tech lead/manager of a Data Science team in the Product organization, leveraging AI tools and functions (e.g., Cortex AI functions, Agents, CoCo) to accelerate product development and data-driven decision-making.
• Mentor team members in core DS disciplines and on the effective and governed use of generative AI tools, including establishing AI best practices and guardrails.
• Drive the team's focus toward AI-native deliverables, such as building out Agentic services, semantic views, and conducting AI evaluation for new product features, strategically shifting away from automatable work.
• Be hands-on by serving as the primary data scientist on projects, specifically focusing on validating the accuracy and output quality of AI-powered analysis and developing POCs for new AI-driven product features.
• Partner with technical and business stakeholders to not only come up with solutions to stated problems, but encourage and enable the team to develop bottoms up ideas.
• Maximize the impact of the team, by making sure the data scientists have the best and correct context, and ensuring their skill sets are properly matched to the project.
• Grow the team, when appropriate. Convince job candidates on why Snowflake, and the product data science team is an awesome place to work

Senior Manager, Product Data Science

• Design and implement optimization models addressing network design, inventory positioning, demand forecasting, and transportation efficiency
• Apply advanced analytics techniques to improve supply chain performance, cost efficiency, service levels, and sustainability outcomes
• Analyze, cleanse, and transform large, imperfect datasets to generate actionable insights
• Develop algorithms using optimization, machine learning, and statistical modeling techniques to solve complex supply chain problems
• Implement production‑ready analytical solutions using Python, R, or comparable programming languages
• Build dashboards and analytical outputs that communicate insights to technical and non‑technical stakeholders
• Collaborate with data scientists, analysts, and architects to ensure scalable and aligned analytical solutions.

Data Scientist – Supply Chain Optimisation

• Pre-sales solution design & value articulation, explaining to clients the analytic details of FICO Scores products, features, analytics and services.
• Partnership with Scores Sales Executives and Client Success Managers to understand client business objectives, use cases, decision criteria, and to ensure informed handoff from presales to post‑sale execution.
• Design and articulate FICO Score solution configurations aligned to client needs, regulatory context, and operational realities.
• Lead technical discovery sessions, solution walkthroughs, and value‑based discussions with client stakeholders.
• Translate analytics, model performance, and score usage into clear business and ROI narratives for executive and technical audiences.
• Support presales activities across new logo, expansion, and renewal motions, including solution positioning and packaging.
• Contribute to pricing discussions by providing technical input, usage assumptions, and solution complexity considerations.
• Partner cross functionally with Sales, Analytics and Clients Success, to support RFx responses, proposals, and deal review materials.
• Review and validate client‑facing materials (presentations, ROI models, solution summaries) for accuracy and relevance.

Director, Presales Data Science

• Apply data science, machine learning, and AI methods to analyze and interpret materials characterization data across microscopy, spectroscopy, diffraction, and related techniques.
• Design, build, and deploy AI agents that automate data analysis tasks, including data ingestion, preprocessing, feature extraction, model selection, and report generation.
• Develop and maintain scalable data workflows, pipelines, and agent-based systems that improve efficiency, reproducibility, and throughput of laboratory data processing.
• Implement agent-driven orchestration of multi-step analytical workflows, enabling autonomous or semi-autonomous execution of scientific data analysis.
• Perform statistical analysis, feature engineering, and exploratory data analysis to identify trends, anomalies, and correlations in experimental datasets.
• Build, validate, and document predictive and classification models for materials characterization, failure analysis, and process optimization.
• Collaborate with scientists and engineers to translate experimental challenges into AI-augmented and agent-enabled solutions.
• Support automation and standardization of data collection, processing, and reporting using both traditional pipelines and intelligent agents.
• Communicate analytical insights and AI-driven results clearly to technical and non-technical stakeholders.
• Contribute to continuous improvement of data infrastructure, AI capabilities, and advanced analytics platforms.

Data Scientist – Materials Characterization, Analytical Sciences, AI Agents

• Collaborate extensively with cross-functional teams to provide the models, data products, and insights necessary for informed decision-making and sustainable growth.
• Leverage comprehensive data resources to extract valuable insights.
• Work alongside leadership to transform business challenges into data-driven solutions.
• Develop metrics, build simple data pipelines, create intuitive dashboards, and generate reports that drive business operations.
• Deliver actionable recommendations through thoughtful analysis and compelling data storytelling that directly impact business outcomes.

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Data Scientist II

Associate Data Scientist

Data Scientist

Senior Data Scientist

Staff Data Scientist

Senior Data Scientist – Hybrid

People Analytics Lead – Recruiting

Senior Director, Data Science

Senior Manager, Data Science – AI Foundations

Head of Data

CD&A Data Scientist

Staff Data Scientist

Ads AI Analytics Lead

Principal Data Scientist, Health Informatics

Manager, Data Science, Forecasting – Routing

Senior Manager, Product Data Science

Data Scientist – Supply Chain Optimisation

Director, Presales Data Science

Data Scientist – Materials Characterization, Analytical Sciences, AI Agents

Senior/Staff Data Scientist, EPD